{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "2bvkQcUOvP5Q"
   },
   "source": [
    "# Logistic regression example\n",
    "### Dr. Tirthajyoti Sarkar, Fremont, CA 94536\n",
    "\n",
    "---\n",
    "\n",
    "This notebook demonstrates solving a logistic regression problem of predicting Hypothyrodism with **Scikit-learn** and **Statsmodels** libraries.\n",
    "\n",
    "The dataset is taken from UCI ML repository.\n",
    "<br>Here is the link: https://archive.ics.uci.edu/ml/datasets/Thyroid+Disease"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "gBvhbvQ3vP5T"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "caPnVuIWvP5a"
   },
   "source": [
    "### Read the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "UBqpn2sFvP5b"
   },
   "outputs": [],
   "source": [
    "names = 'response age sex on_thyroxine query_on_thyroxine antithyroid_medication thyroid_surgery query_hypothyroid query_hyperthyroid pregnant \\\n",
    "sick tumor lithium goitre TSH_measured TSH T3_measured \\\n",
    "T3 TT4_measured TT4 T4U_measured T4U FTI_measured FTI TBG_measured TBG'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "KU4-8YGYvP5f"
   },
   "outputs": [],
   "source": [
    "names = names.split(' ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 210
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 4544,
     "status": "ok",
     "timestamp": 1567214189473,
     "user": {
      "displayName": "Tirthajyoti Sarkar",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AAuE7mD6d7dlMqdpzL4sermIF1ujmpSRxY2WnE4tuB-UsQ=s64",
      "userId": "01914075970409030121"
     },
     "user_tz": 420
    },
    "id": "qSTybVWUwsKq",
    "outputId": "4993f504-2eb9-47e3-dcd1-096ee4d9339c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2019-08-31 01:16:23--  https://raw.githubusercontent.com/tirthajyoti/Machine-Learning-with-Python/master/Datasets/hypothyroid.csv\n",
      "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 151.101.0.133, 151.101.64.133, 151.101.128.133, ...\n",
      "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|151.101.0.133|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 218406 (213K) [text/plain]\n",
      "Saving to: ‘hypothyroid.csv’\n",
      "\n",
      "hypothyroid.csv     100%[===================>] 213.29K  --.-KB/s    in 0.05s   \n",
      "\n",
      "2019-08-31 01:16:23 (4.23 MB/s) - ‘hypothyroid.csv’ saved [218406/218406]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!wget https://raw.githubusercontent.com/tirthajyoti/Machine-Learning-with-Python/master/Datasets/hypothyroid.csv\n",
    "! rm -rf Data/\n",
    "!mkdir Data\n",
    "!mv hypothyroid.csv Data/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "hQxY_7FHvP5i"
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv('Data/hypothyroid.csv',index_col=False,names=names,na_values=['?'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 215
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 260,
     "status": "ok",
     "timestamp": 1567214205482,
     "user": {
      "displayName": "Tirthajyoti Sarkar",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AAuE7mD6d7dlMqdpzL4sermIF1ujmpSRxY2WnE4tuB-UsQ=s64",
      "userId": "01914075970409030121"
     },
     "user_tz": 420
    },
    "id": "IIG7QEoOvP5l",
    "outputId": "ebd8e7fe-63d5-48d4-c85e-ae7538972128"
   },
   "outputs": [
    {
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       "    .dataframe thead th {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>response</th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>on_thyroxine</th>\n",
       "      <th>query_on_thyroxine</th>\n",
       "      <th>antithyroid_medication</th>\n",
       "      <th>thyroid_surgery</th>\n",
       "      <th>query_hypothyroid</th>\n",
       "      <th>query_hyperthyroid</th>\n",
       "      <th>pregnant</th>\n",
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       "      <th>tumor</th>\n",
       "      <th>lithium</th>\n",
       "      <th>goitre</th>\n",
       "      <th>TSH_measured</th>\n",
       "      <th>TSH</th>\n",
       "      <th>T3_measured</th>\n",
       "      <th>T3</th>\n",
       "      <th>TT4_measured</th>\n",
       "      <th>TT4</th>\n",
       "      <th>T4U_measured</th>\n",
       "      <th>T4U</th>\n",
       "      <th>FTI_measured</th>\n",
       "      <th>FTI</th>\n",
       "      <th>TBG_measured</th>\n",
       "      <th>TBG</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>1.48</td>\n",
       "      <td>y</td>\n",
       "      <td>10.0</td>\n",
       "      <td>n</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>hypothyroid</td>\n",
       "      <td>15.0</td>\n",
       "      <td>F</td>\n",
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       "      <td>19.0</td>\n",
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       "      <td>1.13</td>\n",
       "      <td>y</td>\n",
       "      <td>17.0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>hypothyroid</td>\n",
       "      <td>24.0</td>\n",
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       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>y</td>\n",
       "      <td>0.0</td>\n",
       "      <td>y</td>\n",
       "      <td>0.2</td>\n",
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       "      <td>4.0</td>\n",
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       "      <td>y</td>\n",
       "      <td>0.0</td>\n",
       "      <td>n</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>hypothyroid</td>\n",
       "      <td>24.0</td>\n",
       "      <td>F</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>y</td>\n",
       "      <td>430.0</td>\n",
       "      <td>y</td>\n",
       "      <td>0.4</td>\n",
       "      <td>y</td>\n",
       "      <td>6.0</td>\n",
       "      <td>y</td>\n",
       "      <td>1.04</td>\n",
       "      <td>y</td>\n",
       "      <td>6.0</td>\n",
       "      <td>n</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>hypothyroid</td>\n",
       "      <td>77.0</td>\n",
       "      <td>M</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
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       "      <td>57.0</td>\n",
       "      <td>y</td>\n",
       "      <td>1.28</td>\n",
       "      <td>y</td>\n",
       "      <td>44.0</td>\n",
       "      <td>n</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      response   age sex on_thyroxine  ... FTI_measured   FTI TBG_measured TBG\n",
       "0  hypothyroid  72.0   M            f  ...            y  10.0            n NaN\n",
       "1  hypothyroid  15.0   F            t  ...            y  17.0            n NaN\n",
       "2  hypothyroid  24.0   M            f  ...            y   0.0            n NaN\n",
       "3  hypothyroid  24.0   F            f  ...            y   6.0            n NaN\n",
       "4  hypothyroid  77.0   M            f  ...            y  44.0            n NaN\n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "PDof5gIovP5s"
   },
   "outputs": [],
   "source": [
    "to_drop=[]\n",
    "for c in df.columns:\n",
    "    if 'measured' in c or 'query' in c:\n",
    "        to_drop.append(c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 175
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 222,
     "status": "ok",
     "timestamp": 1567214206776,
     "user": {
      "displayName": "Tirthajyoti Sarkar",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AAuE7mD6d7dlMqdpzL4sermIF1ujmpSRxY2WnE4tuB-UsQ=s64",
      "userId": "01914075970409030121"
     },
     "user_tz": 420
    },
    "id": "xyXfEueTvP50",
    "outputId": "a7fbd313-b724-4e24-9b86-3bc815bca035"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['query_on_thyroxine',\n",
       " 'query_hypothyroid',\n",
       " 'query_hyperthyroid',\n",
       " 'TSH_measured',\n",
       " 'T3_measured',\n",
       " 'TT4_measured',\n",
       " 'T4U_measured',\n",
       " 'FTI_measured',\n",
       " 'TBG_measured']"
      ]
     },
     "execution_count": 8,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "to_drop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "VZ0DHD8_vP54"
   },
   "outputs": [],
   "source": [
    "to_drop.append('TBG')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "HHWwfBR-vP59"
   },
   "outputs": [],
   "source": [
    "df.drop(to_drop,axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 195
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 441,
     "status": "ok",
     "timestamp": 1567214208125,
     "user": {
      "displayName": "Tirthajyoti Sarkar",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AAuE7mD6d7dlMqdpzL4sermIF1ujmpSRxY2WnE4tuB-UsQ=s64",
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     "user_tz": 420
    },
    "id": "2iFMV-9TvP6C",
    "outputId": "32cc9c93-b6ae-4cd6-85ca-d0c4e9889233"
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   "outputs": [
    {
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>hypothyroid</td>\n",
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       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>430.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.04</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>hypothyroid</td>\n",
       "      <td>77.0</td>\n",
       "      <td>M</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>7.3</td>\n",
       "      <td>1.2</td>\n",
       "      <td>57.0</td>\n",
       "      <td>1.28</td>\n",
       "      <td>44.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      response   age sex on_thyroxine  ...   T3   TT4   T4U   FTI\n",
       "0  hypothyroid  72.0   M            f  ...  0.6  15.0  1.48  10.0\n",
       "1  hypothyroid  15.0   F            t  ...  1.7  19.0  1.13  17.0\n",
       "2  hypothyroid  24.0   M            f  ...  0.2   4.0  1.00   0.0\n",
       "3  hypothyroid  24.0   F            f  ...  0.4   6.0  1.04   6.0\n",
       "4  hypothyroid  77.0   M            f  ...  1.2  57.0  1.28  44.0\n",
       "\n",
       "[5 rows x 16 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ALZwMuCpvP6J"
   },
   "source": [
    "### Let us see the basic statistics on the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 225
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 301,
     "status": "ok",
     "timestamp": 1567214208755,
     "user": {
      "displayName": "Tirthajyoti Sarkar",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AAuE7mD6d7dlMqdpzL4sermIF1ujmpSRxY2WnE4tuB-UsQ=s64",
      "userId": "01914075970409030121"
     },
     "user_tz": 420
    },
    "id": "4aXISwuJvP6K",
    "outputId": "a600a964-af89-45f2-b8aa-5fcbed933060"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>2717.0</td>\n",
       "      <td>51.154214</td>\n",
       "      <td>19.294405</td>\n",
       "      <td>1.0</td>\n",
       "      <td>35.00</td>\n",
       "      <td>54.00</td>\n",
       "      <td>67.000</td>\n",
       "      <td>98.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TSH</th>\n",
       "      <td>2695.0</td>\n",
       "      <td>5.923180</td>\n",
       "      <td>23.899467</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.70</td>\n",
       "      <td>2.300</td>\n",
       "      <td>530.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T3</th>\n",
       "      <td>2468.0</td>\n",
       "      <td>1.939749</td>\n",
       "      <td>0.996773</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.40</td>\n",
       "      <td>1.80</td>\n",
       "      <td>2.300</td>\n",
       "      <td>10.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TT4</th>\n",
       "      <td>2914.0</td>\n",
       "      <td>108.850000</td>\n",
       "      <td>45.485419</td>\n",
       "      <td>2.0</td>\n",
       "      <td>83.00</td>\n",
       "      <td>104.00</td>\n",
       "      <td>128.000</td>\n",
       "      <td>450.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T4U</th>\n",
       "      <td>2915.0</td>\n",
       "      <td>0.978199</td>\n",
       "      <td>0.226580</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.96</td>\n",
       "      <td>1.065</td>\n",
       "      <td>2.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FTI</th>\n",
       "      <td>2916.0</td>\n",
       "      <td>115.397771</td>\n",
       "      <td>60.239572</td>\n",
       "      <td>0.0</td>\n",
       "      <td>91.00</td>\n",
       "      <td>107.00</td>\n",
       "      <td>129.000</td>\n",
       "      <td>881.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      count        mean        std  min    25%     50%      75%     max\n",
       "age  2717.0   51.154214  19.294405  1.0  35.00   54.00   67.000   98.00\n",
       "TSH  2695.0    5.923180  23.899467  0.0   0.00    0.70    2.300  530.00\n",
       "T3   2468.0    1.939749   0.996773  0.0   1.40    1.80    2.300   10.20\n",
       "TT4  2914.0  108.850000  45.485419  2.0  83.00  104.00  128.000  450.00\n",
       "T4U  2915.0    0.978199   0.226580  0.0   0.85    0.96    1.065    2.21\n",
       "FTI  2916.0  115.397771  60.239572  0.0  91.00  107.00  129.000  881.00"
      ]
     },
     "execution_count": 12,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe().T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "bGTF9UDpvP6U"
   },
   "source": [
    "### Are any data points are missing? We can check it using `df.isna()` method\n",
    "The `df.isna()` method gives back a full DataFrame with Boolean values - True for data present, False for missing data. We can use `sum()` on that DataFrame to see and calculate the number of missing values per column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 316
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 351,
     "status": "ok",
     "timestamp": 1567214209934,
     "user": {
      "displayName": "Tirthajyoti Sarkar",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AAuE7mD6d7dlMqdpzL4sermIF1ujmpSRxY2WnE4tuB-UsQ=s64",
      "userId": "01914075970409030121"
     },
     "user_tz": 420
    },
    "id": "WKbQCqf_vP6X",
    "outputId": "74101b92-c2c9-42f8-9730-ccc590e8962f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "response                    0\n",
       "age                       446\n",
       "sex                        73\n",
       "on_thyroxine                0\n",
       "antithyroid_medication      0\n",
       "thyroid_surgery             0\n",
       "pregnant                    0\n",
       "sick                        0\n",
       "tumor                       0\n",
       "lithium                     0\n",
       "goitre                      0\n",
       "TSH                       468\n",
       "T3                        695\n",
       "TT4                       249\n",
       "T4U                       248\n",
       "FTI                       247\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "tNhUJwc4vP6b"
   },
   "source": [
    "### We can use `df.dropna()` method to drop those missing rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "0TK5dwbGvP6c"
   },
   "outputs": [],
   "source": [
    "df.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 282,
     "status": "ok",
     "timestamp": 1567214211249,
     "user": {
      "displayName": "Tirthajyoti Sarkar",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AAuE7mD6d7dlMqdpzL4sermIF1ujmpSRxY2WnE4tuB-UsQ=s64",
      "userId": "01914075970409030121"
     },
     "user_tz": 420
    },
    "id": "YIQLA-SDvP6f",
    "outputId": "9df571b8-790c-4a61-92b0-34c953206cfb"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2000, 16)"
      ]
     },
     "execution_count": 15,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "KjqJoWCYvP6i"
   },
   "source": [
    "### Creating a transformation function to convert `+` or `-` responses to 1 and 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "prRGSuntvP6k"
   },
   "outputs": [],
   "source": [
    "def class_convert(response):\n",
    "    if response=='hypothyroid':\n",
    "        return 1\n",
    "    else:\n",
    "        return 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "vhZRrgMCvP6s"
   },
   "outputs": [],
   "source": [
    "df['response']=df['response'].apply(class_convert)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 195
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 310,
     "status": "ok",
     "timestamp": 1567214213372,
     "user": {
      "displayName": "Tirthajyoti Sarkar",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AAuE7mD6d7dlMqdpzL4sermIF1ujmpSRxY2WnE4tuB-UsQ=s64",
      "userId": "01914075970409030121"
     },
     "user_tz": 420
    },
    "id": "oMvub8wHvP6v",
    "outputId": "4c4edb6e-6289-4445-b719-f4b9b419079b"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>response</th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>on_thyroxine</th>\n",
       "      <th>antithyroid_medication</th>\n",
       "      <th>thyroid_surgery</th>\n",
       "      <th>pregnant</th>\n",
       "      <th>sick</th>\n",
       "      <th>tumor</th>\n",
       "      <th>lithium</th>\n",
       "      <th>goitre</th>\n",
       "      <th>TSH</th>\n",
       "      <th>T3</th>\n",
       "      <th>TT4</th>\n",
       "      <th>T4U</th>\n",
       "      <th>FTI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>72.0</td>\n",
       "      <td>M</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1.48</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>15.0</td>\n",
       "      <td>F</td>\n",
       "      <td>t</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>145.0</td>\n",
       "      <td>1.7</td>\n",
       "      <td>19.0</td>\n",
       "      <td>1.13</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>24.0</td>\n",
       "      <td>M</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>24.0</td>\n",
       "      <td>F</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>430.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.04</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>77.0</td>\n",
       "      <td>M</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>7.3</td>\n",
       "      <td>1.2</td>\n",
       "      <td>57.0</td>\n",
       "      <td>1.28</td>\n",
       "      <td>44.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   response   age sex on_thyroxine  ...   T3   TT4   T4U   FTI\n",
       "0         1  72.0   M            f  ...  0.6  15.0  1.48  10.0\n",
       "1         1  15.0   F            t  ...  1.7  19.0  1.13  17.0\n",
       "2         1  24.0   M            f  ...  0.2   4.0  1.00   0.0\n",
       "3         1  24.0   F            f  ...  0.4   6.0  1.04   6.0\n",
       "4         1  77.0   M            f  ...  1.2  57.0  1.28  44.0\n",
       "\n",
       "[5 rows x 16 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 87
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 218,
     "status": "ok",
     "timestamp": 1567214213768,
     "user": {
      "displayName": "Tirthajyoti Sarkar",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AAuE7mD6d7dlMqdpzL4sermIF1ujmpSRxY2WnE4tuB-UsQ=s64",
      "userId": "01914075970409030121"
     },
     "user_tz": 420
    },
    "id": "TSg0NNwZvP62",
    "outputId": "6c634c7e-e863-4530-c863-4779628e2330"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['response', 'age', 'sex', 'on_thyroxine', 'antithyroid_medication',\n",
       "       'thyroid_surgery', 'pregnant', 'sick', 'tumor', 'lithium', 'goitre',\n",
       "       'TSH', 'T3', 'TT4', 'T4U', 'FTI'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 19,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "XQPbkEhSvP69"
   },
   "source": [
    "### Exploratory data analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1638,
     "status": "ok",
     "timestamp": 1567214216545,
     "user": {
      "displayName": "Tirthajyoti Sarkar",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AAuE7mD6d7dlMqdpzL4sermIF1ujmpSRxY2WnE4tuB-UsQ=s64",
      "userId": "01914075970409030121"
     },
     "user_tz": 420
    },
    "id": "PKC4kJgivP7C",
    "outputId": "57564cbc-8c27-4143-ca2c-ae17ff03fde5"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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yINS61etspyySJogFIUlqsiC0plu9Spo+FoS81aukJgtiylXVg8CeW71eA5znrV41KZKc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\nNVkQkqSm/wMUSty/jsVKgQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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WzNyTmVszc+uGDRsqLbZfjI2NEREARATbtm2ruSJJTdDrgNgLTBTLE8ANHf3bi7OZzgGe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\nUpKPsQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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zzI8MShkYGEg3UisyIrA9bPvHtpu2P1oiQ1b79+/X7OysNm3aVDoKsMiaNWu0Zs2a0jFS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\nftdXqasAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Ep+qSJD0/nTyL6WOneejykn0T+GSnapEkvXA9f8Og/icf90I5oO/pJwBYOO9lFVdSvfYd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\nhCSplAEhSSplQEiSSv1/ICTrgaVIcCkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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hVUlmZlbumG4+aGZmjcuBYWZmqTgwzMwslZm+N9Sc1nTwgG8NAsz7fekOMGMn+SKx0iNa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\nVBwYZmaWigPDzMxS+f8zu0SskThcgwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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SpFIWhCSplAUhSSplQUiSSlkQkqRSFoQkqZQFIUkq5VpMUg3VYRXVulj4PiysydTrurmi\nrAUh1VCz2eThB7/NBWfNVx2lcqf8on2i47lHvXvkj37S19WvZ0FINXXBWfP86RsPVB1DNfJn3zq7\nq1/PaxCSpFIWhAA4cOAA999/P7t27ao6iqSa8BRTxepyMfKHP/whANdeey2XXHJJZTl67ZaOUp1Z\nEOLAgV+e585MDh48yEtf+tIKE2l2dpZnD/Z1/Zyz6u3Rg32cWdyjuxssiIrV4bfltWvXLhnv3buX\nW265paI0kurCghCHDh067ljdNzw8zHNzj/sqJi3xZ986m1OHh7v29bxILUkqZUGIyy+/fMn4zW9+\nc0VJJNWJBSGuvfbaJeNrrrmmoiSS6sSCEAMDAwwNDQEwNDTEwMBAxYkk1YEFIVqtFq1WC4Cnnnrq\nyLak3mZBiImJCebn24vCzc3NsW3btooTSaoDC0JMTU2RmUD7jXL33HNPxYkk1YHvgxBDQ0PMzMws\nGat6P/qJ76QG2Heo/Xvs0BmHK05SvR/9pI8Lu/j1LAixd+/e447VfY1Go+oItfHzYq2yU1/p9+RC\nuvuzYUGIlStXLplBrFy5srowAuqxBEtdLNxJ7sYbb6w4Se+p1TWIiBiLiB9ERDMirqs6T6/Yt2/f\ncceSelNtCiIi+oDPAu8ELgY+GBEXV5uqN6xZs2bJ+B3veEdFSSTVSW0KArgUaGbmI5n5c+ALwLqK\nM/WEK6644rhjSb2pTtcghoHHFo33AG+qKEtP+cxnPrNkvHXrVm699dZqwqhW6nBDq4Wvv3Atokq9\ndkOrOhXEryQirgKuArjgggsqTvPrYfEF6rKxVKXTTz+96gg9q04FMQucv2i8qti3RGbeBNwEMDo6\nmt2J9uttZGRkSSmMjIxUlkX10ku/LetodboG8R/AhRHxqog4BfgAcEfFmXrC5s2bjzuW1JtqUxCZ\nOQd8Avgn4CFge2Y+WG2q3tBoNI7MGkZGRnyTliSgRgUBkJk7MvO1mfmazPzfVefpJZs3b+bMM890\n9iDpiDpdg1CFGo0Gd911V9UxJNVIrWYQkqT6sCAkSaUsCElSKQtCklQqFu4kdjKKiP3Ao1Xn+DVy\nLvBk1SGkEv5snlivzMzB5Q46qQtCJ1ZETGfmaNU5pOfzZ7ManmKSJJWyICRJpSwILXZT1QGkY/Bn\nswJeg5AklXIGIUkqZUGIiBiLiB9ERDMirqs6j7QgIm6JiCci4oGqs/QiC6LHRUQf8FngncDFwAcj\n4uJqU0lH3AqMVR2iV1kQuhRoZuYjmflz4AvAuoozSQBk5j8DT1Wdo1dZEBoGHls03lPsk9TjLAhJ\nUikLQrPA+YvGq4p9knqcBaH/AC6MiFdFxCnAB4A7Ks4kqQYsiB6XmXPAJ4B/Ah4Ctmfmg9Wmktoi\n4vPAvwG/GRF7ImJD1Zl6ie+kliSVcgYhSSplQUiSSlkQkqRSFoQkqZQFIUkqZUFIkkpZENIi0ea/\nCwkLQiIiRor7YWwDHgD+MCL+LSK+FRG3RcRZxXGfjojvRcR3I+Ivi323RsTfRsR0RPxnRPxesf+0\niPi7iNgdEd+OiN8t9n84Ir4cEZMR8XBE/EWxv6/4XA8Uz/njYv9rimN3RcR9EXFRFd8j9ab+qgNI\nNXEhMA40gS8DqzPz2Yj4FPDJiPgs8AfARZmZEXHOoueO0F42/TXA1yKiAXwcyMz87eI/9Xsi4rXF\n8a8H3gA8B/wgIrYC/w0YzszfAlj0+W8CPpaZD0fEm4C/Aa7s0PdAWsKCkNoezcxvFDOAi4F/jQiA\nU2gv9fAM8DPg5oi4E7hz0XO3Z+Zh4OGIeAS4CHgrsBUgM78fEY8CCwVxb2Y+AxAR3wNeCTwIvLoo\ni7toF8pZwJuB24osAKd25G8vlbAgpLZni48BTGXmB59/QERcCrwdeC/t9asWfpN//no1y61f89yi\n7XmgPzOfjojXAf8d+BjwfuCPgB9n5utfyF9EOlG8BiEt9Q3gLcVpIiLizIh4bfHb/Msycwfwx8Dr\nFj3nfRGxIiJeA7wa+AFwH/Ch4nO8Frig2F8qIs4FVmTml4DNwBsz8wDww4h4X3FMFCUidYUzCGmR\nzNwfER8GPh8RC6dzNgMHgdsj4jTas4xPLnraj4B/B86mfb3gZxHxN8DnImI3MAd8ODOfW3Sq6PmG\ngb9b9AqqPyk+fqj4PJuBl9C+Jez9J+CvKi3L1VylFyEibgXuzMwvVp1FOtE8xSRJKuUMQpJUyhmE\nJKmUBSFJKmVBSJJKWRCSpFIWhCSplAUhSSr1/wCkH5yxWksLawAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for var in ['age','TSH','T3','TT4','T4U','FTI']:\n",
    "    sns.boxplot(x='response',y=var,data=df)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 7581,
     "status": "ok",
     "timestamp": 1567214222969,
     "user": {
      "displayName": "Tirthajyoti Sarkar",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AAuE7mD6d7dlMqdpzL4sermIF1ujmpSRxY2WnE4tuB-UsQ=s64",
      "userId": "01914075970409030121"
     },
     "user_tz": 420
    },
    "id": "Pe0sLCwvvP7P",
    "outputId": "bee1da1c-93b1-41ff-ecbc-cc39274a76ff"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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m0vCf2wn/9Q10TUNx2FDDQWxODzanh6LRD1O9pgz/q4uQ0x3Uqs2UfONfaWxqJic394Rm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TOZhTvfUyec5OePwaZ1RVi0rBBnL3wFlSEX6fkPIaFD3CZ/63twnT0FTVae1Cdu+uQNKXOH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\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UOBwjULrlaQw4XQrDwWIMOF1KAZk6gb4/QmKDzi1CCIkPan8JST2xPO8p2wiJKodjBN57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\nOL4lDKEtq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ehcK4onLgcX8aLt/54BbTDBmFEv5tu5DI5BU2HuUZZ2clZ7YAAUAnvXICcZ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kvntWYt84jb57VkY5f5+YOpGq1Y/pGlghgcz2943jpGF4SpdikQHsBBBNbiw+LxZvNVWr\nppv2D4t22nKLdb2MGNpultRMtPpqWiv3Uvun+VhSMwCJDPjxV39LzTt/pHzxBFOqdbjKSHhhz3nG\nyLhz1O8zb4m1gKNsgUJxZFCRFx2gpGQwaxbMNYd6LZhrGOwDhYH9Z+9eGgJlUWXT/LX7yXW1aRLc\ncPMYWqSNwisnR4RNP8b+4l+R0b+E5kX3kRMnnK7o2sfxle3G89FKZO239K//hP0F2ZQ1CvIvvIva\nzfNN3uywCFlUnmCmi2CzF1tOIcFmLwH3flxDRxtCSdUbZpNS8AOT8bemplH33X8oLd3GhVeNprE1\nEHMFNOfsUdR98HpsfQyvm2yXg4aGBtzbX6IwIkc9nqZGUgocCUH2aZdQsXwqlvSsqBK34fJ64dfh\nsEvN68a9dYFJm6Rq9XQsp1xKYYTjJ+CpiLmCEC9K4tY77qa6MRA39eNAqzD3T3nkgBFJiuQisi80\n7NiALSvXXP5xw2xT6dTwxDHgqcA19Pe0fP1p7BW6rDw9auuL0qjyk5WrppuE3Go2ziGoafS5/klT\n21xDRuN+cyrBoETkHmvWClIONUUEYUeu/aTzsO/+EGfJVTTsWA9ZeWT97DemqlpZp15Ew2fvmKLP\nwnpPuRfcqfqYIik5WOTQuHFj+fMHH7Bs8US9JHt6ti5s62sk2FSPKyeb/vWfMHPVopgRb8Un/oTa\nzfNprfwPwmI1zUErlk2OHQHV2kzRdY+bUg5dQ0aZx4bmelOqdc3GOVhSM03aS7bs/CM231QV2ZKE\nGCKeoIQ8uxoVedFBwgZ7z2d/48M/vW0yuu0/+/zzz8k55njS8/tQUVVtTLhBN15559+BsNno36+f\n8R3f7q8yyqKG9yu49AG8u7YAbQ/xkUQ+0IZF734+6GQ+/NPbiJQMXENGhxwXVVRGeLMdJw2javWM\nKG947rBbyD79UgJ1FYZIaWS6Rv6Fd1FXusT4m6rVj3HnmNHGOTi2KBerw0XQ30r54onse+EWajbN\nNR4Wcs6+IcoLX7lqOhkDzqa6oZmaU2/E6uxlGjjCgmnxIluSCalp1H+8DteQUVhsdnKHjzEcF9Ub\nZusrA6HXwmIl69SLqFg+hap1M01pQXrfmYR760J8ZbuN7483cMaLkvj31/sOuHJwoFWYg0UkKZKL\nyL4QbGmISktrbzsqVz+G4+RzqVg+lf2L7kPYUqh66ylTf6leP4ucs2/AecZI3O8viLKjvS6bZNjH\nsF1tH5EEumDiKScP5OeDTjbuqTDhSCGFAnRHbmDAeTR88rYx/uadfwc5JdeEdC3GUHTt4+QOH0Pd\nX5bgd5eb+qx73RO4hoxSfUzRo/m6vIaia2foFeoyc5BBDUuGkx/3/yGVX++JmkNHcnzfvuQO16vL\ntZ+DuoaOpmrNjKg5pGvYTVEph+3HhtS0NPJ2vMr+l8YZ81JLusM0XsSao37fecuBIlMVCURIxLP9\nT3nZt13dsh6Nirw4RA6mDfDii/O446EnyR8xmRxnIftfuzO22CfCZBClsMbcL5wqEH6IN9XAjqgX\n317ToKggn70Ol1E9pOHTd0wlqDIGnEX18ikUFOThra8n68xrSS0eQGrxALIG/Yb9i+6Lm1qy74Vb\nQGoMG3wqpTt2GlVAatweCq9oW/Vs+PQdvJ9uNKmuB7xuoza3Jc1hiCK1Vn2lPxy3iwpJLR5A1qkX\n4V37CAUFBUktcGRJScU1ZDSej1aiNTfqERipmWhNHmxZebi3LjCEMWvfe5n6j9dhSUmHVEfMa2VJ\nz6Lug4Wm6I1YKwjxRLhaW6NDNNs7QOKtwhysmo8i+Qj3BbvrmDi2o0wXMbSnEmxt1oUNbTa0Jond\nVUTQW0fF8ilILWCsuIX7LcgD2sfwe0tKOlWrZxiO4LCTdfK0+xk4cKBSelcckP/s3YuncQ8iVM43\nnGJpcxaayigG6qtBajgHX62n5NWVM+DE45k85R6mzZlv0hpSfUzR0whHHehO5rYKds0bozWy2jPz\n4clcfN3NiBgC5obYZ0jbzZZdAIiYqcXtxwZHVhafffSBMYev2LmMglw75ZufM6qmWR0uHFaNvHZl\nT0Evc3oommCqsohCcfRQzosO8OKL87h3yqO0tPpBSlzDbibjF7ErX4yfNoP8EZPb0h6yYoeh/bjf\nD03GLy0ltiBYZGRF+CE+oAXRHAU4Bp2Pd9cWQ8CuX26aqdTr+ZdfS1OozQhBipCcWJxHU4uPQlsl\nM99aYZS0PPeK0aRETriaPLHFI/P6UHjFNAKeCv68ZgbZg680zkXdP58g84tSYyDJGvQbgi31VC59\nAKvVRmqKnbtvuZ5F6zebSsXVrJ1BzvBbgdhOmvpty5nz0ERDrT1ZSUvRhasiB/uARy+V2j40Wauv\nRgaDWFJSsKTHL1dLehbVbz+Da8gobLs3xRw4Yw2ylaseA4s4rBBKJUzXM4lny4RFUHTt48b7isUT\nICUTYbEYImkBTwUVSybhGjrasG2WNAdWR+4B7WP4vdRayTn7OlN4v7PkKl5fsYYPx41VDjXFAakq\nL0eTIKw2o3+F+11q8QCjjGLNprkEqr8hY+9Wfnx8MTMffs7oR6qaiKKnE29BpCNzh5KSwRT3ysNT\nXx97XtPSgAz4jHSSeKKgwmI1va+tqeXFF+cxbtzYmEL5xv36xsumUu9XjLqZumYN18XjD6lymlrA\nUSiOHsp5cRBefHEetz/0JAWXPURehIaDNTNXXxVsV0HB1+o3aVPEehgPbn2e51961hTF0Ss3m31r\nZlAwwrxi6CzRS50GPBXYdm9izcrFjPrDHQTOnwpgEtCM9Gp//vnntNBOQ2PNDG676YYoJ0BJyWBm\nTbmH2yc+SBCBzdWbjP5nRa1gVq6ajgz4KV88AUuag/QfnYV760LjAcNxxkg8m1/ShT5DpVUzg028\nv2G1YbBffHEeDTVv0LJ0EkJKjisupF9xPrWh6Aw9vWSUPjms050l2YOv5PUVa5LeefHUww9yx0Mz\nyB/R7pz7W43BObzNmpVH7rBb9DK0y6ZS+eaj9Lr8wbg5n+51TzBryj1RA2e4D1qDrTSsfQRfSwu4\nihEWCyLdGdUHatc+zvKVr3XRGVJ0Z667/gaWvfUOsrUVXzvdm+oNsxG2dHxlu/XcYmch0mJDaD56\njXzUFPLrGnYTdX9ZYpTDC3gqKF82JUpLp719rN4wGxnwk9G/JGolrmLju4ByqCniU9S7GE3qEYI5\nZ11tlFxsP35Xb5hN0FvH1o1rYz6IqD6m6OkcbtRBY4uPlKIfRZfUXv0YMuDHkp6F+/0FgD7Hrn7r\nafJ/d4/5HvU14yvbjdXhonr9LCyOXP574jTmvvI6z8+eady7se7XyEoh7m/mk3vxmChNsI5UIFG2\nQKE4OijnxUG4d8qjFFz2UJRmRe3m+cbkOjKMPjXFHjPtoXzxBOz2FFJT7DwxdSKAqYxSwFNB2ppH\n8Kx6iIAWJDXFzh2jrqJ0xydUbHzX5LXtiFc7VrsLRkzkf8ZP5Mm5L0WFvo0bN5aBAwdy6x138++v\n99Fcs4/MQb81pZpk/vhsvDvfRQb8BFsa8O7cbJQ3DDtHCKWChLc1vP200ab775/AnNdWUHDZNMMR\ntG/NDG6/8HwWrX/eGOisDhdofvIjRM/CDx/JTNg5c++Uh/QoHyEI+lrI+80fKH9jPAgLwmbHlpVP\n1qkXGikhRVdNY//CeylfPAFrVj7B5gbDcQGhB8KLxzN+2iOmaw/mPujwVOBbO4OMfiV4P32bgosn\noHndphBNf4ObP9x1P40tvg6HTyqSn+uuv4GVf9pG0bW6aNp3r95Ozaa5SL/PSHWyOlyG3Qx4KkAL\ngDU6XS6jfwm1775IzYqpBNN04VoCPnKG/t4UUZEx4JfUbn7JcJ66hoymrnTJERX4/T4lhBWJRWnp\nNn41ZBhkOrGkZWB1uLBm5pJz9ig8H60k0FSv21+LFSEEVkcevXKzVD9QKOJwOFEHpaXbqCjfT2vg\nOxwDz6Fi2WSkDOqiicJK71FPoXnd1JUuwf3eq0jNj9S0mONNxdIHsTl7IQM+Y2GwpgORE5Ei5vGq\n8ynhTYWi61DOi4PQ0hog7wC51u0nxk9MnRi1eu4pXUbu8DFk9C+h6YtS7pz0CEKAzC4kx+vG5ixE\n87ppzcgjUFtGWoqdPGcmq97ZQlFBPvfedguvr1jDqD/cQVFBPiWnnszfX2mb2DtOGoZt9yZuuGMM\nPzvjbP699xtafP7Y7RZWvRxmHAOe7XTRu7dGeXkFTXs+ovCqR7A5C/GV7abug4XG+7B3Wwu13+Ys\nRNhTTTW4bc5Csi64h9+P+29ee/H/8cz81+h19XQ0r9t4CBEZTua+spBN697k/imP8Onn/4fmKIgq\nadVTqlSMGzeWcePG8uKL87j74afJHHojnm3LsaRnR1VwyTr1IjwfraTXZQ9iSU3Hkp6NxWaH9KyY\nOaDN0sZ3zVa+3b2XIRdcSrC1mezTL8UZ6Zi7ZCLliydSFKr4YnMWGmlCNZvmghDsLqvBmuGkPvsU\nzr1iNLOm3GM4XtTD3uGRqOdv2VvvkDt8jHFfS3+LKdUpTLDFazg6ZaAVRGpMZwMWC3lXTDO2l80b\nGzOiwrvzXSMVBSCn5Bpq1s4g75I2+/t984wjV98OJVxYkTiUlm7jrLPOxt7rOFOkT7hCVjiFr2ze\nWPIvvNvQYOm7Z2UXt1yh6N4cStRBeNz7el8Z1dW1BIQNtBacZ16B77t/4ThpGO6tC7GkZ1G9fhYA\n+RfdbYrCyym5xiSUCyCRIc0kQdWaxxH2VD1K+KTzDhg5EVkpJF51vrqqKkpLt6mxQKHoAlS1kYMg\npBa3ykcsJeJx48Z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54MCB33t1uaRkMOveeLnD4qYKM+Hzd+sdd/PvN6ciJVgtgrwsB31t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\nWf53NQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x1080 with 42 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(data=df[df.columns[1:]],diag_kws={'edgecolor':'k','bins':25},plot_kws={'edgecolor':'k'})\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "kkUKMvPgEUFW"
   },
   "source": [
    "### Create dummy variables for the categorical variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "cmnKaugfvP7T"
   },
   "outputs": [],
   "source": [
    "df_dummies = pd.get_dummies(data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 321,
     "status": "ok",
     "timestamp": 1567214225491,
     "user": {
      "displayName": "Tirthajyoti Sarkar",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AAuE7mD6d7dlMqdpzL4sermIF1ujmpSRxY2WnE4tuB-UsQ=s64",
      "userId": "01914075970409030121"
     },
     "user_tz": 420
    },
    "id": "6LPTF7VCvP7Y",
    "outputId": "8a424b31-19e2-42fd-f7aa-31ec683ea099"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2000, 25)"
      ]
     },
     "execution_count": 23,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_dummies.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 363
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 365,
     "status": "ok",
     "timestamp": 1567214226054,
     "user": {
      "displayName": "Tirthajyoti Sarkar",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AAuE7mD6d7dlMqdpzL4sermIF1ujmpSRxY2WnE4tuB-UsQ=s64",
      "userId": "01914075970409030121"
     },
     "user_tz": 420
    },
    "id": "Cn3vcIoWvP7c",
    "outputId": "5d182ce2-adf9-4528-f1eb-dff3e80dc896"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>response</th>\n",
       "      <th>age</th>\n",
       "      <th>TSH</th>\n",
       "      <th>T3</th>\n",
       "      <th>TT4</th>\n",
       "      <th>T4U</th>\n",
       "      <th>FTI</th>\n",
       "      <th>sex_F</th>\n",
       "      <th>sex_M</th>\n",
       "      <th>on_thyroxine_f</th>\n",
       "      <th>on_thyroxine_t</th>\n",
       "      <th>antithyroid_medication_f</th>\n",
       "      <th>antithyroid_medication_t</th>\n",
       "      <th>thyroid_surgery_f</th>\n",
       "      <th>thyroid_surgery_t</th>\n",
       "      <th>pregnant_f</th>\n",
       "      <th>pregnant_t</th>\n",
       "      <th>sick_f</th>\n",
       "      <th>sick_t</th>\n",
       "      <th>tumor_f</th>\n",
       "      <th>tumor_t</th>\n",
       "      <th>lithium_f</th>\n",
       "      <th>lithium_t</th>\n",
       "      <th>goitre_f</th>\n",
       "      <th>goitre_t</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2723</th>\n",
       "      <td>0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.2</td>\n",
       "      <td>333.0</td>\n",
       "      <td>0.74</td>\n",
       "      <td>450.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>496</th>\n",
       "      <td>0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>116.0</td>\n",
       "      <td>1.06</td>\n",
       "      <td>109.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1002</th>\n",
       "      <td>0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>145.0</td>\n",
       "      <td>0.81</td>\n",
       "      <td>180.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1243</th>\n",
       "      <td>0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.8</td>\n",
       "      <td>86.0</td>\n",
       "      <td>1.05</td>\n",
       "      <td>82.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1732</th>\n",
       "      <td>0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>1.9</td>\n",
       "      <td>104.0</td>\n",
       "      <td>1.04</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1295</th>\n",
       "      <td>0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.6</td>\n",
       "      <td>95.0</td>\n",
       "      <td>0.80</td>\n",
       "      <td>119.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>1</td>\n",
       "      <td>70.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>58.0</td>\n",
       "      <td>1.03</td>\n",
       "      <td>56.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2654</th>\n",
       "      <td>0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>117.0</td>\n",
       "      <td>1.04</td>\n",
       "      <td>113.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>234</th>\n",
       "      <td>0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>1.3</td>\n",
       "      <td>130.0</td>\n",
       "      <td>1.05</td>\n",
       "      <td>124.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1925</th>\n",
       "      <td>0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0.86</td>\n",
       "      <td>63.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      response   age   TSH   T3  ...  lithium_f  lithium_t  goitre_f  goitre_t\n",
       "2723         0  72.0   0.0  3.2  ...          1          0         1         0\n",
       "496          0  34.0   0.0  2.3  ...          1          0         1         0\n",
       "1002         0  57.0   0.0  1.9  ...          1          0         1         0\n",
       "1243         0  68.0   0.0  2.8  ...          1          0         1         0\n",
       "1732         0  53.0   1.3  1.9  ...          1          0         1         0\n",
       "1295         0  29.0   0.5  1.6  ...          1          0         1         0\n",
       "71           1  70.0  13.0  1.8  ...          1          0         1         0\n",
       "2654         0  59.0   0.0  1.6  ...          1          0         1         0\n",
       "234          0  63.0   0.3  1.3  ...          1          0         1         0\n",
       "1925         0   7.0   0.4  2.3  ...          1          0         1         0\n",
       "\n",
       "[10 rows x 25 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_dummies.sample(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "cOtuodYBvP7g"
   },
   "source": [
    "### Test/train split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "tknuBZDnvP7h"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(df_dummies.drop('response',axis=1), \n",
    "                                                    df_dummies['response'], test_size=0.30, \n",
    "                                                    random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 52
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 316,
     "status": "ok",
     "timestamp": 1567214228757,
     "user": {
      "displayName": "Tirthajyoti Sarkar",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AAuE7mD6d7dlMqdpzL4sermIF1ujmpSRxY2WnE4tuB-UsQ=s64",
      "userId": "01914075970409030121"
     },
     "user_tz": 420
    },
    "id": "G2RA5KJcEe1j",
    "outputId": "82e7fe03-1a48-4531-f479-29e25338e94b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set shape (1400, 24)\n",
      "Test set shape (600, 24)\n"
     ]
    }
   ],
   "source": [
    "print(\"Training set shape\",X_train.shape)\n",
    "print(\"Test set shape\",X_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "udN1_fDGvP7j"
   },
   "source": [
    "### Using `LogisticRegression` estimator from Scikit-learn\n",
    "We are using the L2 regularization by default"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "P62kZToSvP7k"
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "GGqsx2JQvP7n"
   },
   "outputs": [],
   "source": [
    "clf1 = LogisticRegression(penalty='l2',solver='newton-cg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 105
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 342,
     "status": "ok",
     "timestamp": 1567214231539,
     "user": {
      "displayName": "Tirthajyoti Sarkar",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AAuE7mD6d7dlMqdpzL4sermIF1ujmpSRxY2WnE4tuB-UsQ=s64",
      "userId": "01914075970409030121"
     },
     "user_tz": 420
    },
    "id": "OKvgysYbvP7r",
    "outputId": "7b71f038-75d0-416d-cbd3-cd49147d7a12"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
       "                   multi_class='warn', n_jobs=None, penalty='l2',\n",
       "                   random_state=None, solver='newton-cg', tol=0.0001, verbose=0,\n",
       "                   warm_start=False)"
      ]
     },
     "execution_count": 29,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf1.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "92jXObyGEtqv"
   },
   "source": [
    "### Intercept, coefficients, and score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 366,
     "status": "ok",
     "timestamp": 1567214232993,
     "user": {
      "displayName": "Tirthajyoti Sarkar",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AAuE7mD6d7dlMqdpzL4sermIF1ujmpSRxY2WnE4tuB-UsQ=s64",
      "userId": "01914075970409030121"
     },
     "user_tz": 420
    },
    "id": "cE5kZZfKvP7u",
    "outputId": "f44fa099-46a7-4462-dc0a-596da617502c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2.28502971])"
      ]
     },
     "execution_count": 30,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf1.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 105
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 203,
     "status": "ok",
     "timestamp": 1567214233526,
     "user": {
      "displayName": "Tirthajyoti Sarkar",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AAuE7mD6d7dlMqdpzL4sermIF1ujmpSRxY2WnE4tuB-UsQ=s64",
      "userId": "01914075970409030121"
     },
     "user_tz": 420
    },
    "id": "Tl_QpokTvP70",
    "outputId": "75fca93c-b77f-4a4f-d3dd-482cd6727b26"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.02031893,  0.01861482, -0.3252159 ,  0.00395534,  0.30699787,\n",
       "        -0.10796205,  0.30204784, -0.30203619,  0.89773279, -0.89772114,\n",
       "         0.62687938, -0.62686773, -0.67442642,  0.67443807,  0.13429321,\n",
       "        -0.13428156,  0.27313209, -0.27312044,  0.26138875, -0.2613771 ,\n",
       "         0.08933295, -0.0893213 , -0.2489876 ,  0.24899925]])"
      ]
     },
     "execution_count": 31,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf1.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 159,
     "status": "ok",
     "timestamp": 1567214234087,
     "user": {
      "displayName": "Tirthajyoti Sarkar",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AAuE7mD6d7dlMqdpzL4sermIF1ujmpSRxY2WnE4tuB-UsQ=s64",
      "userId": "01914075970409030121"
     },
     "user_tz": 420
    },
    "id": "JNLVBLeBvP75",
    "outputId": "4be8c0aa-4600-4327-bdd4-1b9460ec49d4"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9816666666666667"
      ]
     },
     "execution_count": 32,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf1.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Bcd0se4pvP7_"
   },
   "source": [
    "### For `LogisticRegression` estimator, there is a special `predict_proba` method which computes the raw probability values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "x-bfhAPhFhC-"
   },
   "outputs": [],
   "source": [
    "prob_threshold = 0.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 343
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 431,
     "status": "ok",
     "timestamp": 1567214236819,
     "user": {
      "displayName": "Tirthajyoti Sarkar",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AAuE7mD6d7dlMqdpzL4sermIF1ujmpSRxY2WnE4tuB-UsQ=s64",
      "userId": "01914075970409030121"
     },
     "user_tz": 420
    },
    "id": "iyhdw03vvP8A",
    "outputId": "7417014a-a08f-4205-93d6-bc4504806ea1"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Prob of NO</th>\n",
       "      <th>Prob of YES</th>\n",
       "      <th>Decision</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.990471</td>\n",
       "      <td>0.009529</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.998714</td>\n",
       "      <td>0.001286</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.999999</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.624016</td>\n",
       "      <td>0.375984</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.999296</td>\n",
       "      <td>0.000704</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.864075</td>\n",
       "      <td>0.135925</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.983696</td>\n",
       "      <td>0.016304</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.998507</td>\n",
       "      <td>0.001493</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.221967</td>\n",
       "      <td>0.778033</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.999968</td>\n",
       "      <td>0.000032</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Prob of NO  Prob of YES  Decision\n",
       "0    0.990471     0.009529         0\n",
       "1    0.998714     0.001286         0\n",
       "2    0.999999     0.000001         0\n",
       "3    0.624016     0.375984         0\n",
       "4    0.999296     0.000704         0\n",
       "5    0.864075     0.135925         0\n",
       "6    0.983696     0.016304         0\n",
       "7    0.998507     0.001493         0\n",
       "8    0.221967     0.778033         1\n",
       "9    0.999968     0.000032         0"
      ]
     },
     "execution_count": 34,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prob_df=pd.DataFrame(clf1.predict_proba(X_test[:10]),columns=['Prob of NO','Prob of YES'])\n",
    "prob_df['Decision']=(prob_df['Prob of YES']>prob_threshold).apply(int)\n",
    "prob_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 210
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 322,
     "status": "ok",
     "timestamp": 1567214239165,
     "user": {
      "displayName": "Tirthajyoti Sarkar",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AAuE7mD6d7dlMqdpzL4sermIF1ujmpSRxY2WnE4tuB-UsQ=s64",
      "userId": "01914075970409030121"
     },
     "user_tz": 420
    },
    "id": "A4SQ1XXxvP8E",
    "outputId": "4375fcde-e8b2-4b6a-8aca-f39eaa9091a3"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2944    0\n",
       "511     0\n",
       "2116    0\n",
       "1412    0\n",
       "2039    0\n",
       "2013    0\n",
       "1478    0\n",
       "2744    0\n",
       "83      1\n",
       "2100    0\n",
       "Name: response, dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "_PeE-kOIvP8J"
   },
   "source": [
    "### Classification report, and confusion matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "DqHMkTpfvP8K"
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report, confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 175
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 333,
     "status": "ok",
     "timestamp": 1567214241804,
     "user": {
      "displayName": "Tirthajyoti Sarkar",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AAuE7mD6d7dlMqdpzL4sermIF1ujmpSRxY2WnE4tuB-UsQ=s64",
      "userId": "01914075970409030121"
     },
     "user_tz": 420
    },
    "id": "7Qv9q7vZvP8P",
    "outputId": "dc659624-750a-46a0-a2e4-fda2d075b026"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.99      0.99       569\n",
      "           1       0.86      0.77      0.81        31\n",
      "\n",
      "    accuracy                           0.98       600\n",
      "   macro avg       0.92      0.88      0.90       600\n",
      "weighted avg       0.98      0.98      0.98       600\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, clf1.predict(X_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 106
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 326,
     "status": "ok",
     "timestamp": 1567214242563,
     "user": {
      "displayName": "Tirthajyoti Sarkar",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AAuE7mD6d7dlMqdpzL4sermIF1ujmpSRxY2WnE4tuB-UsQ=s64",
      "userId": "01914075970409030121"
     },
     "user_tz": 420
    },
    "id": "XSNxqNpHvP8a",
    "outputId": "ef5c5238-c34c-48b3-c2bb-4bc61a1d6b20"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Predict-YES</th>\n",
       "      <th>Predict-NO</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>YES</th>\n",
       "      <td>565</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NO</th>\n",
       "      <td>7</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Predict-YES  Predict-NO\n",
       "YES          565           4\n",
       "NO             7          24"
      ]
     },
     "execution_count": 38,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(confusion_matrix(y_test, clf1.predict(X_test)),columns=['Predict-YES','Predict-NO'],index=['YES','NO'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "feE3XMmFvP8g"
   },
   "source": [
    "### Using `statsmodels` library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "PPb2I3nUvP8i"
   },
   "outputs": [],
   "source": [
    "import statsmodels.formula.api as smf\n",
    "import statsmodels.api as sm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 122
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 313,
     "status": "ok",
     "timestamp": 1567214245871,
     "user": {
      "displayName": "Tirthajyoti Sarkar",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AAuE7mD6d7dlMqdpzL4sermIF1ujmpSRxY2WnE4tuB-UsQ=s64",
      "userId": "01914075970409030121"
     },
     "user_tz": 420
    },
    "id": "aSDnuENRvP8p",
    "outputId": "e8423394-7ab1-4ab6-f184-878e288148d0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['response', 'age', 'TSH', 'T3', 'TT4', 'T4U', 'FTI', 'sex_F', 'sex_M',\n",
       "       'on_thyroxine_f', 'on_thyroxine_t', 'antithyroid_medication_f',\n",
       "       'antithyroid_medication_t', 'thyroid_surgery_f', 'thyroid_surgery_t',\n",
       "       'pregnant_f', 'pregnant_t', 'sick_f', 'sick_t', 'tumor_f', 'tumor_t',\n",
       "       'lithium_f', 'lithium_t', 'goitre_f', 'goitre_t'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 40,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_dummies.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "KtujrgvyvP8u"
   },
   "source": [
    "### Create a 'formula' in the same style as in R language"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "vIC8zIl7vP8v"
   },
   "outputs": [],
   "source": [
    "formula = 'response ~ ' + '+'.join(df_dummies.columns[1:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 54
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 387,
     "status": "ok",
     "timestamp": 1567214247707,
     "user": {
      "displayName": "Tirthajyoti Sarkar",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AAuE7mD6d7dlMqdpzL4sermIF1ujmpSRxY2WnE4tuB-UsQ=s64",
      "userId": "01914075970409030121"
     },
     "user_tz": 420
    },
    "id": "Hiyjw4g7vP8z",
    "outputId": "ea586ab3-dedc-4e70-8410-4b9c1303d812"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'response ~ age+TSH+T3+TT4+T4U+FTI+sex_F+sex_M+on_thyroxine_f+on_thyroxine_t+antithyroid_medication_f+antithyroid_medication_t+thyroid_surgery_f+thyroid_surgery_t+pregnant_f+pregnant_t+sick_f+sick_t+tumor_f+tumor_t+lithium_f+lithium_t+goitre_f+goitre_t'"
      ]
     },
     "execution_count": 42,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "formula"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "_A9t8JJZvP86"
   },
   "source": [
    "### Fit a GLM (Generalized Linear model) with this formula and choosing `Binomial` as the family of function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "k5Oi9XTQvP87"
   },
   "outputs": [],
   "source": [
    "model = smf.glm(formula = formula, data=df_dummies, family=sm.families.Binomial())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "dWlUR6aQvP9C"
   },
   "outputs": [],
   "source": [
    "result=model.fit()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Q-yoBbVUvP9M"
   },
   "source": [
    "### `summary` method shows a R-style table with all kind of statistical information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 721
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 335,
     "status": "ok",
     "timestamp": 1567214250552,
     "user": {
      "displayName": "Tirthajyoti Sarkar",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AAuE7mD6d7dlMqdpzL4sermIF1ujmpSRxY2WnE4tuB-UsQ=s64",
      "userId": "01914075970409030121"
     },
     "user_tz": 420
    },
    "id": "1pO4jVRhvP9N",
    "outputId": "c9b29d2c-5c04-4ec3-ca65-e42a8cf61cbe"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 Generalized Linear Model Regression Results                  \n",
      "==============================================================================\n",
      "Dep. Variable:               response   No. Observations:                 2000\n",
      "Model:                            GLM   Df Residuals:                     1984\n",
      "Model Family:                Binomial   Df Model:                           15\n",
      "Link Function:                  logit   Scale:                          1.0000\n",
      "Method:                          IRLS   Log-Likelihood:                -123.59\n",
      "Date:                Sat, 31 Aug 2019   Deviance:                       247.17\n",
      "Time:                        01:17:27   Pearson chi2:                 9.10e+03\n",
      "No. Iterations:                    22                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "============================================================================================\n",
      "                               coef    std err          z      P>|z|      [0.025      0.975]\n",
      "--------------------------------------------------------------------------------------------\n",
      "Intercept                   -3.1161   4981.805     -0.001      1.000   -9767.274    9761.042\n",
      "age                          0.0219      0.011      2.062      0.039       0.001       0.043\n",
      "TSH                          0.0214      0.008      2.823      0.005       0.007       0.036\n",
      "T3                          -0.0233      0.320     -0.073      0.942      -0.651       0.604\n",
      "TT4                          0.0367      0.027      1.376      0.169      -0.016       0.089\n",
      "T4U                         -1.5693      1.656     -0.947      0.343      -4.816       1.677\n",
      "FTI                         -0.1420      0.030     -4.709      0.000      -0.201      -0.083\n",
      "sex_F                       -1.4005   2490.902     -0.001      1.000   -4883.479    4880.678\n",
      "sex_M                       -1.7156   2490.902     -0.001      0.999   -4883.794    4880.363\n",
      "on_thyroxine_f              -1.2357   2490.902     -0.000      1.000   -4883.315    4880.843\n",
      "on_thyroxine_t              -1.8804   2490.902     -0.001      0.999   -4883.959    4880.198\n",
      "antithyroid_medication_f    -0.3206   2490.902     -0.000      1.000   -4882.400    4881.758\n",
      "antithyroid_medication_t    -2.7954   2490.902     -0.001      0.999   -4884.874    4879.284\n",
      "thyroid_surgery_f           -2.2489   2490.902     -0.001      0.999   -4884.328    4879.830\n",
      "thyroid_surgery_t           -0.8672   2490.902     -0.000      1.000   -4882.946    4881.212\n",
      "pregnant_f                  -0.2489   2490.903  -9.99e-05      1.000   -4882.329    4881.831\n",
      "pregnant_t                  -2.8671   2490.903     -0.001      0.999   -4884.947    4879.213\n",
      "sick_f                      -1.1899   2490.902     -0.000      1.000   -4883.269    4880.889\n",
      "sick_t                      -1.9262   2490.902     -0.001      0.999   -4884.005    4880.153\n",
      "tumor_f                      8.1294   6275.938      0.001      0.999   -1.23e+04    1.23e+04\n",
      "tumor_t                    -11.2454   7357.670     -0.002      0.999   -1.44e+04    1.44e+04\n",
      "lithium_f                    8.7704   2.42e+04      0.000      1.000   -4.75e+04    4.75e+04\n",
      "lithium_t                  -11.8864   2.91e+04     -0.000      1.000    -5.7e+04     5.7e+04\n",
      "goitre_f                    -1.9841   2490.902     -0.001      0.999   -4884.063    4880.095\n",
      "goitre_t                    -1.1320   2490.902     -0.000      1.000   -4883.211    4880.947\n",
      "============================================================================================\n"
     ]
    }
   ],
   "source": [
    "print(result.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "RauDe86hvP9U"
   },
   "source": [
    "### The `predict` method computes probability for the test dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 210
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 435,
     "status": "ok",
     "timestamp": 1567214252008,
     "user": {
      "displayName": "Tirthajyoti Sarkar",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AAuE7mD6d7dlMqdpzL4sermIF1ujmpSRxY2WnE4tuB-UsQ=s64",
      "userId": "01914075970409030121"
     },
     "user_tz": 420
    },
    "id": "woKRF8eSvP9V",
    "outputId": "c13d015e-72fa-49bd-e7a5-609c5fdde41e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2944    2.408353e-03\n",
       "511     6.250161e-04\n",
       "2116    6.125699e-07\n",
       "1412    3.274420e-01\n",
       "2039    7.888963e-04\n",
       "2013    1.325293e-01\n",
       "1478    3.197375e-02\n",
       "2744    1.098108e-03\n",
       "83      8.650453e-01\n",
       "2100    6.524129e-05\n",
       "dtype: float64"
      ]
     },
     "execution_count": 46,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.predict(X_test[:10])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "O8nEHxDBvP9d"
   },
   "source": [
    "### To create binary predictions, you have to apply a threshold probability and convert the booleans into integers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "NAuhv8qivP9i"
   },
   "outputs": [],
   "source": [
    "y_pred=(result.predict(X_test)>prob_threshold).apply(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 175
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 349,
     "status": "ok",
     "timestamp": 1567214253529,
     "user": {
      "displayName": "Tirthajyoti Sarkar",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AAuE7mD6d7dlMqdpzL4sermIF1ujmpSRxY2WnE4tuB-UsQ=s64",
      "userId": "01914075970409030121"
     },
     "user_tz": 420
    },
    "id": "AbETGp2VvP9l",
    "outputId": "bb934a46-589b-452c-cb51-d703db368835"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.99      0.99       569\n",
      "           1       0.89      0.77      0.83        31\n",
      "\n",
      "    accuracy                           0.98       600\n",
      "   macro avg       0.94      0.88      0.91       600\n",
      "weighted avg       0.98      0.98      0.98       600\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 106
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 320,
     "status": "ok",
     "timestamp": 1567214254421,
     "user": {
      "displayName": "Tirthajyoti Sarkar",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AAuE7mD6d7dlMqdpzL4sermIF1ujmpSRxY2WnE4tuB-UsQ=s64",
      "userId": "01914075970409030121"
     },
     "user_tz": 420
    },
    "id": "uMxjluvgvP9u",
    "outputId": "c804be83-7fdb-45b7-b9da-5ff9243fe434"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Predict-YES</th>\n",
       "      <th>Predict-NO</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>YES</th>\n",
       "      <td>566</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NO</th>\n",
       "      <td>7</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Predict-YES  Predict-NO\n",
       "YES          566           3\n",
       "NO             7          24"
      ]
     },
     "execution_count": 49,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(confusion_matrix(y_test, y_pred),columns=['Predict-YES','Predict-NO'],index=['YES','NO'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "60jxAabLvP9y"
   },
   "source": [
    "### A smaller model with only first few variables\n",
    "\n",
    "We saw that majority of variables in the logistic regression model has p-values very high and therefore they are not statistically significant. We create another smaller model removing those variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 319,
     "status": "ok",
     "timestamp": 1567214256044,
     "user": {
      "displayName": "Tirthajyoti Sarkar",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AAuE7mD6d7dlMqdpzL4sermIF1ujmpSRxY2WnE4tuB-UsQ=s64",
      "userId": "01914075970409030121"
     },
     "user_tz": 420
    },
    "id": "dJW9zPjWvP90",
    "outputId": "72ebfc6e-ab4e-4d66-8fd4-ea8862f83f3f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'response ~ age+TSH+T3+TT4+T4U+FTI'"
      ]
     },
     "execution_count": 50,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "formula = 'response ~ ' + '+'.join(df_dummies.columns[1:7])\n",
    "formula"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 404
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 381,
     "status": "ok",
     "timestamp": 1567214256675,
     "user": {
      "displayName": "Tirthajyoti Sarkar",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AAuE7mD6d7dlMqdpzL4sermIF1ujmpSRxY2WnE4tuB-UsQ=s64",
      "userId": "01914075970409030121"
     },
     "user_tz": 420
    },
    "id": "Ujsp2wTdvP93",
    "outputId": "911a9dc3-6754-48b1-cf17-f5ae5641c11c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 Generalized Linear Model Regression Results                  \n",
      "==============================================================================\n",
      "Dep. Variable:               response   No. Observations:                 2000\n",
      "Model:                            GLM   Df Residuals:                     1993\n",
      "Model Family:                Binomial   Df Model:                            6\n",
      "Link Function:                  logit   Scale:                          1.0000\n",
      "Method:                          IRLS   Log-Likelihood:                -131.55\n",
      "Date:                Sat, 31 Aug 2019   Deviance:                       263.09\n",
      "Time:                        01:17:34   Pearson chi2:                 5.65e+03\n",
      "No. Iterations:                     9                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept      4.1596      1.995      2.085      0.037       0.250       8.069\n",
      "age            0.0248      0.010      2.481      0.013       0.005       0.044\n",
      "TSH            0.0212      0.007      2.892      0.004       0.007       0.035\n",
      "T3             0.0704      0.297      0.237      0.812      -0.511       0.652\n",
      "TT4            0.0296      0.026      1.134      0.257      -0.022       0.081\n",
      "T4U           -1.2903      1.665     -0.775      0.438      -4.553       1.973\n",
      "FTI           -0.1307      0.029     -4.449      0.000      -0.188      -0.073\n",
      "==============================================================================\n"
     ]
    }
   ],
   "source": [
    "model = smf.glm(formula = formula, data=df_dummies, family=sm.families.Binomial())\n",
    "result=model.fit()\n",
    "print(result.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 175
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 296,
     "status": "ok",
     "timestamp": 1567214257371,
     "user": {
      "displayName": "Tirthajyoti Sarkar",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AAuE7mD6d7dlMqdpzL4sermIF1ujmpSRxY2WnE4tuB-UsQ=s64",
      "userId": "01914075970409030121"
     },
     "user_tz": 420
    },
    "id": "BFhjN31yvP95",
    "outputId": "c1e38dfa-18e9-4ec8-b019-e7a8934eefc6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.99      0.99       573\n",
      "           1       0.74      0.85      0.79        27\n",
      "\n",
      "    accuracy                           0.98       600\n",
      "   macro avg       0.87      0.92      0.89       600\n",
      "weighted avg       0.98      0.98      0.98       600\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_pred=(result.predict(X_test)>prob_threshold).apply(int)\n",
    "print(classification_report(y_pred,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 106
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 405,
     "status": "ok",
     "timestamp": 1567214259057,
     "user": {
      "displayName": "Tirthajyoti Sarkar",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AAuE7mD6d7dlMqdpzL4sermIF1ujmpSRxY2WnE4tuB-UsQ=s64",
      "userId": "01914075970409030121"
     },
     "user_tz": 420
    },
    "id": "Wz6is-LwvP98",
    "outputId": "fdf0b67b-11dc-4fd5-c0dd-bd9b6dbd4ca6"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Predict-YES</th>\n",
       "      <th>Predict-NO</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>YES</th>\n",
       "      <td>565</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NO</th>\n",
       "      <td>8</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Predict-YES  Predict-NO\n",
       "YES          565           4\n",
       "NO             8          23"
      ]
     },
     "execution_count": 53,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(confusion_matrix(y_test, y_pred),columns=['Predict-YES','Predict-NO'],index=['YES','NO'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "BX1DrOE2P-X1"
   },
   "source": [
    "### How does the confusion matrix change when we modulate the probability threshold?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 545
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 435,
     "status": "ok",
     "timestamp": 1567214456910,
     "user": {
      "displayName": "Tirthajyoti Sarkar",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AAuE7mD6d7dlMqdpzL4sermIF1ujmpSRxY2WnE4tuB-UsQ=s64",
      "userId": "01914075970409030121"
     },
     "user_tz": 420
    },
    "id": "cGQETc_OQLbY",
    "outputId": "7cd0c67f-0e57-4e25-ea2a-df8a0802a5a6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix for probability threshold 0.5\n",
      "----------------------------------------------------------------------------------------------------\n",
      "     Predict-YES  Predict-NO\n",
      "YES          565           4\n",
      "NO             8          23\n",
      "\n",
      "Confusion matrix for probability threshold 0.6\n",
      "----------------------------------------------------------------------------------------------------\n",
      "     Predict-YES  Predict-NO\n",
      "YES          565           4\n",
      "NO            13          18\n",
      "\n",
      "Confusion matrix for probability threshold 0.7\n",
      "----------------------------------------------------------------------------------------------------\n",
      "     Predict-YES  Predict-NO\n",
      "YES          567           2\n",
      "NO            16          15\n",
      "\n",
      "Confusion matrix for probability threshold 0.8\n",
      "----------------------------------------------------------------------------------------------------\n",
      "     Predict-YES  Predict-NO\n",
      "YES          567           2\n",
      "NO            17          14\n",
      "\n",
      "Confusion matrix for probability threshold 0.9\n",
      "----------------------------------------------------------------------------------------------------\n",
      "     Predict-YES  Predict-NO\n",
      "YES          568           1\n",
      "NO            21          10\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for prob_threshold in [0.5,0.6,0.7,0.8,0.9]:\n",
    "  y_pred=(result.predict(X_test)>prob_threshold).apply(int)\n",
    "  df=pd.DataFrame(confusion_matrix(y_test, y_pred),columns=['Predict-YES','Predict-NO'],index=['YES','NO'])\n",
    "  print(\"Confusion matrix for probability threshold {}\".format(prob_threshold))\n",
    "  print(\"-\"*100)\n",
    "  print(df)\n",
    "  print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "wZgi_pXGHD3E"
   },
   "source": [
    "### How do the probabilities compare between `Scikit-learn` and `Statsmodels` predictions? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "O55cr8UrHqnM"
   },
   "outputs": [],
   "source": [
    "sklearn_prob = clf1.predict_proba(X_test)[...,1][:10]\n",
    "statsmodels_prob =  result.predict(X_test[:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 343
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 328,
     "status": "ok",
     "timestamp": 1567214514360,
     "user": {
      "displayName": "Tirthajyoti Sarkar",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AAuE7mD6d7dlMqdpzL4sermIF1ujmpSRxY2WnE4tuB-UsQ=s64",
      "userId": "01914075970409030121"
     },
     "user_tz": 420
    },
    "id": "KYumGbsAHDCp",
    "outputId": "3331d555-b758-480c-f4a9-bcf88fa8fb42"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Scikit-learn Prob</th>\n",
       "      <th>Statsmodels Prob</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.009529</td>\n",
       "      <td>0.026742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.001286</td>\n",
       "      <td>0.000780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.000001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.375984</td>\n",
       "      <td>0.306287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.000704</td>\n",
       "      <td>0.000797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.135925</td>\n",
       "      <td>0.132377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.016304</td>\n",
       "      <td>0.007412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.001493</td>\n",
       "      <td>0.001065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.778033</td>\n",
       "      <td>0.874865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.000032</td>\n",
       "      <td>0.000161</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Scikit-learn Prob  Statsmodels Prob\n",
       "0           0.009529          0.026742\n",
       "1           0.001286          0.000780\n",
       "2           0.000001          0.000001\n",
       "3           0.375984          0.306287\n",
       "4           0.000704          0.000797\n",
       "5           0.135925          0.132377\n",
       "6           0.016304          0.007412\n",
       "7           0.001493          0.001065\n",
       "8           0.778033          0.874865\n",
       "9           0.000032          0.000161"
      ]
     },
     "execution_count": 56,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prob_comp_df=pd.DataFrame(data={'Scikit-learn Prob':list(sklearn_prob),'Statsmodels Prob':list(statsmodels_prob)})\n",
    "prob_comp_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "q_nNkT6NvP9-"
   },
   "source": [
    "### Coefficient interpretation\n",
    "\n",
    "What is the interpretation of the coefficient value for `age` and `FTI`?\n",
    "\n",
    "- With every one year of age increase, the **log odds of the hypothyrodism increases by 0.0248** or the odds of hypothyroidsm increases by a factor of exp(0.0248) = 1.025 i.e. almost 2.5%.\n",
    "- With every one unit of FTI increase, the **log odds of the hypothyrodism decreases by 0.1307** or the odds of hypothyroidsm decreases by a factor of exp(0.1307) = 1.1396 i.e. almost by 12.25%."
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    "### Food for thought\n",
    "\n",
    "<img src=\"https://raw.githubusercontent.com/tirthajyoti/Stats-Maths-with-Python/master/images/food-for-thought.png\" width=200 height=130></img>\n",
    "\n",
    "<br>\n",
    "\n",
    "When doing **classification with deep neural networks**, should you also examine the raw probabilities (outputs from the last `sigmoid` layer for example)?\n",
    "\n",
    "This approach may be helpful for deep learning tasks, when the output of deep learning is fed to another business decision process, **where the cost of Type-I and Type-II error are vastly different**.\n",
    "\n"
   ]
  }
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